Active Pattern Recognition Using Genetic Programming

@PhdThesis{Teredesai:thesis,
author = "Ankur Mukund Teredesai",
title = "Active Pattern Recognition Using Genetic Programming",
school = "State University of New York at Buffalo",
year = "2002",
address = "Buffalo, New York, USA",
month = sep,
keywords = "genetic algorithms, genetic programming",
URL = "http://phdtree.org/pdf/25644579-active-pattern-recognition-using-genetic-programming/",
URL = "http://search.proquest.com/docview/305243136",
size = "156 pages",
abstract = "The need for faster and robust methods for pattern
recognition and data mining is ever increasing.
Classical machine learning algorithms have always been
used in a variety of domains like optical character
recognition (OCR), speech recognition and information
extraction.
Different levels of informative detail can be present
in different regions of a pattern image. Classifiers
which selectively use features corresponding to
discriminating regions in making decisions for
particular classes are called active classifiers.
Design of active classifiers requires the pattern
recognition technique to blend feature discovery within
the classifier training phase. This dual task of
feature discovery and classifier training can be
combined to make the learning algorithm adaptive. This
dissertation titled Active Pattern Recognition using
Genetic Programming highlights the need for
applications to be adaptive. Traditional machine
learning algorithms for classification can be made
dynamic in terms of feature selection, computational
resource and scalability. This dissertation describes
how to make one such algorithm (Genetic Programming)
active, scalable and recurrent. The proposed extensions
are used to develop classifiers for handwritten digit
recognition. Genetic programming (GP) is a biologically
motivated machine learning technique like genetic
algorithms (GA). The essential idea is to represent
states (classification models in our case) as
chromosomes (encoded as expression trees) and to evolve
a population of new offspring trees by selectively
pairing parent trees. We first illustrate how GP based
active classifiers are developed for handwritten digit
recognition. A two-stage classification method
motivated by pair-wise confusion between digits is then
explored. Inspired by the performance for off-line hand
written digit classification, a strategy to classify
on-line handwritten digits based on off-line features
and GP is developed. We then present a recurrent-GP
framework which extends the proposed active pattern
recognition paradigm for applications where the length
of the feature vector is dynamic. One of the key
deterrents in using evolutionary computation techniques
for complex real-world applications in pattern
recognition and data mining is their non-scalable
nature in terms of computational requirements. We have
designed a new Efficient-GP technique to address these
issues. The dissertation concludes by discussing the
role of this paradigm in computational machine learning
theory.",
notes = "http://www.cedar.buffalo.edu/papers/dissertations.html
Doctoral Dissertations
supervisor: Venu Govindaraju
http://genealogy.math.ndsu.nodak.edu/id.php?id=104577
UMI Microform 3076535",
}